Data Science for Mental Health (DS4MH) @ The Alan Turing Institute
About Us
The vision for this interest group is to kick-start one or more projects using contemporary data science and multi-modal data for mental health to provide insight and benefit for individuals, clinicians, and contribute to fundamental research in mental health (including dementia) as well as the data science methodology. It aims to provide an informal bridge between clinicians, charities, and data owners (like CRIS, UKDP, and Biobank) and data science researchers to stimulate and align cutting edge research in this area.
Events
Meetings
We organise monthly meetings (including half-an-hour long invited talks) at the Turing. Meetings are organised and moderated by Jenny Chim, Yue Wu, and Emilio Ferrucci. Please join our mailing list for more updated information.
As a part of AI UK Fringe, we jointly organised a hybrid event with the NLP interest group on AI for Mental Health Monitoring on 28th March 2024.
See here for our previous talks.
Upcoming Events
Meetings
| Date | Time | Presenter | Title |
|---|---|---|---|
| 2025.07.17 | 15:00 | Introduction | |
| 15:05 | Dr. Pat Pataranutaporn (MIT) |
Designing Human-AI Interactions for Promoting Human Flourishing
Creating AI systems that augment human capabilities and promote personal and societal flourishing demands expertise in multiple research fields. My research takes an interdisciplinary, human-centered approach to the development of personal AI systems and the understanding of the complexity of human-AI interaction. More specifically, my research: (1) Creates novel AI prototypes through personalized multi-modal systems that support human flourishing by facilitating learning and enhancing well-being; (2) Examining the science of human augmentation by AI systems through large-scale experimental studies that reveal the effects of AI on human decision-making, sense-making, behavior, beliefs, sense of self, and other critical aspects; (3) Proposes new techniques including novel platforms and tools that others can use to implement human augmentation systems; and (4) Develops novel research methods that combine qualitative and quantitative analyses of human-AI interaction. The goal of my work is to establish a new discipline that focuses on the science of human-AI interaction for human augmentation and empowers AI developers with a more informed understanding of the implications of design choices for AI systems that interact with humans. My hope is to catalyze a new intellectual renaissance and contribute to the advancement of AI that benefits the human experience. |
|
| 15:50 | After talks discussion |